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Cloud detection from visual band of satellite image based on variance of fractal dimension

Journal of Systems Engineering and Electronics 2019年第3期30卷 485-491页

作者： TIAN Pingfang GUANG Qiang LIU Xing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

关键词： cloud detection visual image satellite image variance of local fractal dimension (VLFD)

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Area Charging for Wireless Rechargeable Sensors

Area Charging for Wireless Rechargeable Sensors

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International Conference on Computer Communications and Networks (ICCCN)

作者： Haipeng Dai Xiaoyu Wang Lijie Xu Chao Dong Qian Liu Lei Meng Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing CHINA Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing CHINA Nanjing University of Aeronautics and Astronautics Nanjing CHINA

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

In this paper, we consider the problem of area charging, that is, assuming that there is a mobile charger (MC) equipped with a directional wireless charger whose charging area is in the shape of a sector, and the MC can only recharge sensors on the boundary of an Area of Interest (AOI), how to design an efficient charging scheme for the MC to recharge any sensor inside the AOI with efficient energy while its overall charging time is minimized. To address this problem, we first partition the AOI into two types of subareas, i.e., rectangle-like subareas and sector-like subareas, based on the Medial Axis of the AOI. Second, we propose rectangle-based moving strategy for the rectangle-like subareas, and sector-based rotating strategy for the sector-like subareas, respectively, for the MC. Finally, we calculate charging time of all subareas based on the above two moving strategies, and prove that our algorithm achieves a constant approximation ratio. We evaluate our algorithm by conducting extensive simulation. The results show that on average, other comparison algorithms require at least 18:70 times of charging time of that of our algorithm.

关键词： Sensors Wireless sensor networks Wireless communication Communication system security Approximation algorithms Shape Security

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Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

arXiv

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arXiv 2021年

作者： Luo, Hongming Zhou, Fei Liao, Guangsen Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Key Laboratory of Digital Creative Technology China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Guangdong Shenzhen China

In real-world applications, such as sharing photos on social media platforms, images are always not only sub-sampled but also heavily compressed thus often containing various artefacts. Simple methods for enhancing the resolution of such images will exacerbate the artefacts, rendering them visually objectionable. In spite of its high practical values, super-resolving compressed images is not well studied in the literature. In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artefacts removal and resolution enhancement. Based on a mathematical inference model for estimating a clean low-resolution (LR) image and a clean high-resolution (HR) image from a down-sampled and compressed observation, we have designed a CISR architecture consisting of two deep neural network modules: the artefacts removal module (ARM) and the resolution enhancement module (REM). The ARM and the REM work in parallel with both taking the compressed LR image as their inputs, at the same time they also work in series with the REM taking the output of the ARM as one of its inputs and the ARM taking the output of the REM as its other input. A technique called unfolding is introduced to recursively suppress the compression artefacts and restore the image resolution. A unique feature of our CISR system is that it exploits the parallel and series connections between the ARM and the REM, and recursive optimization to reduce the model's dependency on specific types of degradation thus making it possible to train a single model to super-resolve images compressed by different methods to different qualities. Experiments are conducted on a mixture of JPEG and WebP compressed images without assuming apriori compression type and compression quality factor. To demonstrate our technique's real-world application value, we have also applied the trained models directly to restore social media images which have undergone scaling and compre

关键词： Deep neural networks

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Correction: Copper particle-free ink with enhanced performance for inkjet-printed flexible UWB antennas

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Journal of Materials Chemistry C 2023年第44期11卷 15774-15774页

作者： Wendong Yang Zhichao Dong Zihao Guo Haoqiang Sun School of Electronic and Information Engineering Liaoning Technical University No. 188 Longwan South Street Huludao City 125105 Liaoning China Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao City 125105 Liaoning China

Correction for ‘Copper particle-free ink with enhanced performance for inkjet-printed flexible UWB antennas’ by Wendong Yang et al., J. Mater. Chem. C, 2023, 11, 14429–14438, https://***/10.1039/D3TC02515B.

Correction for ‘Copper particle-free ink with enhanced performance for inkjet-printed flexible UWB antennas’ by Wendong Yang et al., J. Mater. Chem. C, 2023, 11, 14429–14438, https://***/10.1039/D3TC02515B.

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Image reconstruction from photoacoustic projections

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Photonics Insights 2024年第3期3卷 145-209页

作者： Chao Tian Kang Shen Wende Dong Fei Gao Kun Wang Jiao Li Songde Liu Ting Feng Chengbo Liu Changhui Li Meng Yang Sheng Wang Jie Tian Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiChina School of Engineering Science University of Science and Technology of ChinaHefeiChina Department of Anesthesiology the First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing HefeiChina College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina School of Information Science and Technology ShanghaiTech UniversityShanghaiChina Shanghai Clinical Research and Trial Center ShanghaiChina CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina School of Precision Instruments and Optoelectronics Engineering Tianjin UniversityTianjinChina Academy for Engineering and Technology Fudan UniversityShanghaiChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenChina Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Departments of Ultrasound State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina School of Engineering Medicine Beihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina

Photoacoustic computed tomography(PACT)is a rapidly developing biomedical imaging modality and has attracted substantial attention in recent *** reconstruction from photoacoustic projections plays a critical role in image formation in *** we review six major classes of image reconstruction approaches developed in the past three decades,including delay and sum,filtered back projection,series expansion,time reversal,iterative reconstruction,and deep-learning-based *** principal ideas and implementations of the algorithms are summarized,and their reconstruction performances under different imaging scenarios are *** challenges,future directions,and perspectives for the development of image reconstruction algorithms in PACT are also *** review provides a self-contained reference guide for beginners and specialists in the photoacoustic community,to facilitate the development and application of novel photoacoustic image reconstruction algorithms.

关键词： optical imaging photoacoustic tomography image reconstruction deep learning

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A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure 15th

A New Ontology Meta-Matching Technique with a Hybrid Semanti...

引用

15th International Conference on intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Lu, Jiawei Xue, Xingsi Lin, Guoxiang Huang, Yikun College of Information Science and Engineering Fujian University of Technology Fuzhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou China Concord University College Fujian Normal University Fuzhou China

ISBN: (纸本)9789811397097

Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Ontology

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Effect of respiratory microflorae colonization on short and long-time outcomes of respiratory syncytial virus infection in children: A scoping review

Pediatric Discovery

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Pediatric Discovery 2024年第4期2卷

作者： Lidan Gan Enmei Liu Yu Deng Department of Respiratory Medicine Children's Hospital of Chongqing Medical University Chongqing China Ministry of Education Key Laboratory of Child Development and Disorders Children's Medical Big Data Intelligent Application Center Department of Respiratory Medicine Chongqing China National Clinical Research Center for Child Health and Disorders Chongqing China

Respiratory syncytial virus (RSV) is an essential cause of lower respiratory tract infection in children under 2 years of age, especially under 6 months. In decades, studies have shown that the respiratory tract microflorae with RSV infection were related to disease severity and played a role in the development of recurrent wheezing, but the effect of respiratory microflorae on RSV infection are still underestimated. This study aims to conclude the effect of respiratory microflorae colonization on RSV infectious disease severity and recurrent wheezing and provide suggestions for future research directions from the perspective of respiratory tract florae. We conducted a scoping review. Studies were eligible if they reported on the effect of microflorae on RSV infectious diseases among children. We exacted the following information: title, publication time, first author's country, and article type. We finally included 33 articles in this scoping review. The number of studies rapidly increased since 2013 and the highest number of hospitalizations were reported in children <2 years. More than half (69.70%) were conducted in America and most studies are original studies (57.58%). The Review highlighted that the respiratory microflorae played an important role in RSV infectious disease severity and recurrent wheezing. We found that Streptococcus pneumoniae (***), Haemophilus influenza (HI), Moraxella catarrhalis (***), and Staphylococcus aureus (SA) were the dominant profiles in children with RSV infection. Understanding the respective role of respiratory microflorae on RSV infection and its mechanisms would improve prevention and treatment strategies from the perspective of microflorae.

关键词： childhood asthma disease severity microflorae recurrent wheezing respiratory syncytial virus

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Design of Train Carriage Abnormal Noise Alarm System Based on Zigbee 3

Design of Train Carriage Abnormal Noise Alarm System Based o...

引用

2019 3rd International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2019

作者： Zhang, Yongsheng Liu, Miaomiao School of Computer and Information Technology Northeast Petroleum University Daqing Heilongjiang163318 China Key Laboratory for Oil Big Data and Intelligent Analysis of Heilongjiang Province Daqing Heilongjiang163318 China

In order to realize the timely and accurate alarm for abnormal conditions in train carriages, a scheme of abnormal noise alarm system based on zigbee is proposed. The overall system architecture as well as the hardware and software structure of each node is designed. Meanwhile, the workflow and implementation process of each module are elaborated in detail. The system includes five parts: sensor node, zigbee router, coordinator, video module and intelligent terminal. Firstly, the decibels collected by the sound sensor are processed through threshold judgment and sent to the coordinator through the zigbee router module. Secondly, the video module recognizes and analyzes the video of the alarming carriage by means of artificial intelligence. Finally, the detailed alarm information is sent to the intelligent terminal. The development cost of this system is low while its installation and maintenance is convenient. It improves the monitoring facilities of train carriages, which can not only guarantee the accuracy and real time of the warning, but also set the threshold value and delete alarm mark manually. It has strong expansibility and portability, which is of practical significance for improving the safety of train operation. © Published under licence by IOP Publishing Ltd.

关键词： Zigbee

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Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification

引用

IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第6期39卷 1930-1941页

作者： Xu, Bolei Liu, Jingxin Hou, Xianxu Liu, Bozhi Garibaldi, Jon Ellis, Ian O. Green, Andy Shen, Linlin Qiu, Guoping Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518060 Peoples R China Histo Pathol Diagnost Ctr Shanghai 200444 Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG7 2RD England Univ Nottingham Fac Med & Hlth Sci Nottingham NG7 2RD England Shenzhen Univ Comp Vis Inst Sch Comp Sci & Software Engn Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image. However, these hard-attention based approaches usually take a long time to converge with weak guidance, and valueless patches may be trained by the classifier. To overcome this problem, we propose a deep selective attention approach that aims to select valuable regions in the original images for classification. In our approach, a decision network is developed to decide where to crop and whether the cropped patch is necessary for classification. These selected patches are then trained by the classification network, which then provides feedback to the decision network to update its selection policy. With such a co-evolution training strategy, we show that our approach can achieve a fast convergence rate and high classification accuracy. Our approach is evaluated on a public breast cancer histopathological image database, where it demonstrates superior performance compared to state-of-the-art deep learning approaches, achieving approximately 98% classification accuracy while only taking 50% of the training time of the previous hard-attention approach.

关键词： Histopathological image reinforcement learning breast cancer classification deep learning

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Utility maximization for multihop wireless networks employing BATS codes

arXiv

引用

arXiv 2021年

作者： Dong, Yanyan Jin, Sheng Chen, Yanzuo Yang, Shenghao Yin, Hoover H.F. The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Carnegie Mellon University The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China

BATS (BATched Sparse) codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding. Due to both the probably nonconcave objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems cannot be applied directly to solve our NUM problem. We introduce a two-step algorithm to solve our NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm. © 2021, CC BY-NC-ND.

关键词： Packet loss

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